1. Field
Certain aspects of the present disclosure generally relate to neural networks and, more particularly, to operating a spiking neural network composed of one or more neurons, wherein a single neuron is capable of computing any general transformation to any arbitrary precision.
2. Background
An artificial neural network is a mathematical or computational model composed of an interconnected group of artificial neurons (i.e., neuron models). Artificial neural networks may be derived from (or at least loosely based on) the structure and/or function of biological neural networks, such as those found in the human brain. Because artificial neural networks can infer a function from observations, such networks are particularly useful in applications where the complexity of the task or data makes designing this function by hand impractical.
One type artificial neural network is the spiking neural network, which incorporates the concept of time into its operating model, as well as neuronal and synaptic state, thereby increasing the level of realism in this type of neural simulation. Spiking neural networks are based on the concept that neurons fire only when a membrane potential reaches a threshold. When a neuron fires, it generates a spike that travels to other neurons which, in turn, raise or lower their membrane potentials based on this received spike.
Traditionally, information was thought to be coded largely, if not exclusively, in the rate of firing of a neuron. If information is coded in neuron firing rate, there may be significant computational overhead to model neurons with membrane dynamics, spiking events with temporal precision, and spike-timing dependent plasticity (STDP) compared to merely modeling neurons as firing rate transforms with rate-based learning rules, such as the Oja rule.